N. A.B. Osman

Bio: N. A.B. Osman is an academic researcher. The author has contributed to research in topics: Double inverted pendulum & Double pendulum. The author has an hindex of 1, co-authored 1 publications receiving 31 citations.

Intelligent Control for Self-erecting Inverted Pendulum Via Adaptive Neuro-fuzzy Inference System

Abstract: A self-erecting single inverted pendulum (SESIP) is one of typical nonlinear systems. The control scheme running the SESIP consists of two main control loops. Namely, these control loops are swing-up controller and stabilization controller. A swing-up controller of an inverted pendulum system must actuate the pendulum from the stable position. While a stabilization controller must stand the pendulum in the unstable position. To deal with this system, a lot of control techniques have been used on the basis of linearized or nonlinear model. In real-time implementation, a real inverted pendulum system has state constraints and limited amplitude of input. These problems make it difficult to design a swing-up and a stabilization controller. In this paper, first, the mathematical models of cart and single inverted pendulum system are presented. Then, the Position-Velocity controller is designed to swing-up the pendulum considering physical behavior. For stabilizing the inverted pendulum, a Takagi-Sugeno fuzzy controller with Adaptive Neuro-Fuzzy Inference System (ANFIS) architecture is used to guarantee stability at unstable equilibrium position. Experimental results are given to show the effectiveness of these controllers.

Identification and Control of Nonlinear Systems using Soft Computing Techniques

Abstract: The inverted pendulum is a highly nonlinear and open-loop unstable system. This means that standard linear techniques cannot model the nonlinear dynamics of the system, Inverted pendulum system is often used as a benchmark for verifying the performance and effectiveness of a new control method because of the simplicities of the structure. In this paper an accurate model of the inverted pendulum system, a neural network controller and ANFIS (Adaptive Neuro-Fuzzy Inference System) controller to stabilize the system have been developed. A control law that removes some of the nonlinearities from the process and allows the process to exhibit its dynamics has been developed. This aids in stabilizing the nonlinear pendulum. The quality of the data input has also been improved, since only limited number of variables that can be measured accurately are included in the system identification Simulation results establishes that the proposed controller has good set point tracking and disturbance rejection properties.

Dynamic modeling and control of a two wheeled robotic vehicle with a virtual payload

Abstract: One of the challenging issues to consider in balancing a two-wheeled robotic machine (TWRM) is when the load carried by the machine is changing position along the vehicle intermediate body (IB). An issue of interest in this case is the resulting impact on the system behaviour due to changing position of the load. Further complications arise with changing the size of the load. This work presents investigations into controlling a TWRM with a payload positioned at different locations along its IB. Two types of control techniques are developed and implemented on the system, the traditional proportional-derivative (PD) control and fuzzy logic (FL) control. PD and PD-fuzzy logic control techniques are developed to balance the vehicle with a payload incorporating two different scenarios. Firstly, the payload is positioned at different locations along its IB. Secondly, it is considered to perform a continuous sliding motion along the IB. The balancing of the robot has to be achieved during the motion of the vehicle and the payload along the IB. An external disturbance force is applied to the rod which constitutes the IB in order to test the robustness of the developed controllers. Investigations are carried out on the effect of changing the level and duration of the disturbance force, and changing the speed of the payload on the system during the balancing mode. Simulation results of both control algorithms are analyzed on a comparative basis.

Self-erecting inverted pendulum employing PSO for stabilizing and tracking controller

Abstract: The main objective of this paper is to design a state feedback controller for stabilizing and tracking control of self-erecting inverted pendulum employing intelligent method using particle swarm optimization (PSO). This is motivated by the fact that one has to face trial and error approach in conventional feedback control design by pole placement method or linear quadratic regulator (LQR) method via Riccati equation. The simulation results show the effectiveness of the proposed method. The proposed state feedback controller works jointly with swing-up controller in the self-erecting inverted pendulum system.